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Episode 1481: Multisport Sabermetrics Exchange (NASCAR and Cycling)
Date January 2, 2020 Summary In the sixth installment of a special, seven-episode series on the past, present, and future of advanced analysis in non-baseball sports, Ben Lindbergh talks to Motorsports Analytics founder and The Athletic writer David Smith about NASCAR and then former Garmin-Sharp and Team Sky analyst Robby Ketchell about cycling (47:48), touching on the origins of sabermetrics-style analysis in each sport, the major challenges, big breakthroughs, and overturned misconceptions, the early adopters, the cutting-edge stats and tech, the level of acceptance within the game, the effects on the spectator experience, the parallels with baseball, and more, plus a postscript on Bucky Dent, Rich Hill, the Duckworth-Lewis-Stern method, and ESPN Bat Track. Topics * Interview with David Smith * David's work as a NASCAR talent scout * History and ease of analysis of NASCAR * Importance of computer simulations in race success * Racing's transition from a hobby to a sport * Financial recession as a driver of analytics in NASCAR * What makes a good driver? * Production in Equal Equipment Rating (PEER) * Isolating driver impact * Erik Jones and Christopher Bell * Importance of crew chiefs and engineers * Track types * Changes to passing frequency * Comparing across eras * Safety improvements * Kyle Busch and Trevor Bauer * End of race strategy * Interview with Robby Ketchell * History and easy of analysis in cycling * Advancements in power meter technology * Team construction and rider strengths * Suit technolgy * Analytics to improve training * Partnerships with F1 racing and sailing * Difficulty of closing the talent gap with analytics * Robby's work on the Eliud Kipchoge marathon record attempt * Episode 1480 follow-up: Bucky Dent and famous non walk-off home runs * Rich Hill signing * Episode 1476 follow-up: Duckworth-Lewis-Stern method * Smash factor and ESPN's early use of bat speed in telecasts Intro Grateful Dead, "The Race is On" Interstitial Yo La Tengo, "The Race is on Again" Outro George Harrison, "Faster" Notes * Unlike many other professional sports in the U.S., NASCAR does not have a traditional draft for getting young talent to enter the sport. * David rates NASCAR an 8/10 for its ease of analysis. He says that the sports' collective use of analytics has been influenced by the increased presence of engineers on team staffs. * After the recession in 2008 NASCAR teams made use of analytics to find inefficiencies in spending. This led to many teams to not pay for older, more established drivers that did not actually lead to better team results. * Loop data at NASCAR tracks was gathered in 2005. David says this data tracks any movement all cars on the track make. Technology also exists to pair this with GPS data. * David says that a good driver will have talent in the corners and will be able to give clear feedback to team members about how the car is driving. * Age 39 is the statistical peak for a NASCAR driver. * The fastest car in a race wins 40% of the time. * Unlike baseball, where a run is worth the same regardless of inning, taking the lead is most valuable at the end of the race. Restarts erase many gains, since the pack compresses for the restart. * David would like to see transaction analysis for NASCAR signings. However there is currently no public salary data and no drivers' union. * Robby rates current cycling analysis a 6 or 7 out of 10. However he says that the sport gathers enough data that it could be a 10/10, but teams are not fully using all the data they gather. * With data usage and technological advancements Robby says teams have to decide if they would like to develop more general strategies for their team or ones specific for top riders. Each team member has different strengths and different roles over the course of a several day/week road race. * Physiological and power meter data is private to cycling teams. * Analytics have led to a variety of small improvements in cycling, but Robby can't think of anything groundbreaking that upended the conventional wisdom. * Analytics have improved the spectator experience, giving fans a better understanding of why teams make the moves they do. (The teams have always done them, but analytics makes it easier to understand why.) * Robby thinks that analytics have made the biggest impact on developing new training techniques. However he does not think that analytics can be used to close the talent gap between riders. Rather, he feels that it has actually helped amplify the strengths of the existing top riders. * Rich Hill has signed with the Twins, which creates the (admittedly small) possibility of a Hill/Astudillo battery. * The Duckworth-Lewis-Stern Method is an example of advanced statistical techniques entering the official rules of cricket. It improves how a team's score is adjusted to compensate for time lost to rain delays. * In 2000, ESPN used Bat Track, which was developed by Sportvision, the company behind PITCHF/X and other sport technologies. Like cricket's Smash Factor, Bat Track calculated bat speed. * Ben contacted someone who worked on Bat Track. It used multiple radars to get accurate measurements. There was no observed correlation between pitch/bat speed and the outcome of the hit. Links * Effectively Wild Episode 1481: Multisport Sabermetrics Exchange (NASCAR and Cycling) * David Smith at The Athletic * Motorsports Analytics by David Smith * Positive Regression Podcast * Truths behind a win drought, regression and a top prospect: What analytics taught us about NASCAR in 2019 by David Smith * Four points from three races: How Erik Jones can move forward following a fruitless playoff performance by David Smith * The 2019 Stat Darlings: Identifying the NASCAR drivers with sparkling statistical profiles by David Smith * Hail to the crew chiefs: Recognizing the ‘Stat Darlings’ among NASCAR’s race-callers in 2019 by David Smith * Tale of the tape: Which metrics matter and who has the advantage in NASCAR’s championship race by David Smith * Andrew Maness Leading A NASCAR Statistics Revolution by Matt Weaver * Meet Team Sky’s data genius who can predict what’ll happen during a race by James Witts * Big Data and Decision Optimisation for Racing by Semi-Pro Cycling * Next Level Tech: A Peek into Cutting Edge PRO Technology by Semi-Pro Cycling * The Outer Line: Researchers seek the power to believe pro cyclists by Joe Harris and Steve Maxwell * What can we learn from Chris Froome’s power data? by Michael Hutchinson * Select Committee's report darkens clouds over Team Sky and Brailsford by Daniel Benson * Team Sky was good enough to win the ethical way. So why didn’t it? by Michael Hutchinson * Portsmouth man is scientist behind under 2-hour marathon by Mike Zhe * Duckworth-Lewis-Stern Method Wikipedia page * ESPN Bat Track telecast * Tracking the Sweet Spot by David Pescovitz Category:Episodes Category:Guest Episodes